Computational modelling of drugs for Alzheimer’s disease (AD) and applications on artificial neural network systems (ANN); NETS

Abstract

Alzheimer’s disease (AD) is an irreversible and progressive disease that affects neurons and their connections in parts of the brain, specifically the hippocampus and entorhinal cortex. The purpose of this research is to modify current medications of Alzheimer’s disease with the use of computational modelling. The modifications are concluded to improve the half maximal inhibitory concentration (IC 50 ) value which is the concentration needed for the drug to inhibit a specific biological function. Drug design throughout this research has been done on the computational modelling software Gaussian 09. The expected modified IC 50 values are predicted using two methods. First, the functional graph methods utilizing the energies and the experimentally measured IC 50 values producing correlations that result in predicted IC 50 values for the modified drug molecules. The second method involves using an artificial neural network system NETS to predict the IC 50 values of modified drug molecules. Four modified drug molecules resulted in promising outcomes in which the IC 50 values were improved with a value of one order of magnitude and higher. The data obtained shows that computational modelling can be a novel time-saving and significant step for drug discovery.